Prior art wood characteristics detection systems require experts using rules, image processing techniques, or combinations of them. These extracted features are often used as inputs to machine learning algorithms. However, the effort to derive and select a minimum set of extracted features for use in the detection process to maximize accuracy is difficult, time consuming, and not guaranteed for accuracy. The introduction of deep learning has removed a need to perform these tasks because they are done automatically as part of the learning process.
Grading board lumber defects requires that many wood characteristics, for example, knots be identified no matter what their orientation in the board (generalization). The effects of a knot on the strength of a board depend on how close the knot is to the edge and how much clear wood it displaces (location and size). It is much more difficult to teach a computer to grade board lumber than it is to teach a person. Human beings have billions of brain connections that make them experts in pattern matching. After inspecting many thousands of knots, a person can discriminate from 16 ft. (4.88 m) away the difference between a #1 knot and a #2 knot on a 2 in. (5 cm)×4 in. (10.2 cm) board.
Computer vision systems must be programmed to identify a knot. Knot heads are mostly, but not always, ovals and circles. Knots sometimes have a blonde ring. Knot edges can be obscured by pitch and stain. For dimension lumber, knot heads have to be associated with other knot heads on different faces. A person learns this task by observations that make an image in the person's mind, which filters out unimportant distractors and emphasizes significant points. It is difficult to program a computer to carry out this process.
Computers process numbers, and people process images. There is nothing in the numbers that indicates whether a particular object is important. The computer vision system looks at everything and tries to discover knots in a vast set of numbers. A computer programmer of a computer vision system attempts to anticipate all possible presentations of wood characteristics, such as knots, and then gives explicit program instructions as to how to handle exceptional cases. Modifications to programs are exhaustively tested to ensure that any changes made result in actual improvement. In spite of these difficulties, automatic grading systems introduced during the past ten years do acceptable work but are fragile and need constant improvement and maintenance.